News recommendations heavily rely on Natural Language Processing (NLP) methods to analyze, understand, and categorize content, enabling personalized suggestions based on user interests and reading behaviors. Large Language Models (LLMs) like GPT-4 have shown promising performance in understanding natural language. However, the extent of their applicability to news recommendation systems remains to be validated. This paper introduces RecPrompt, the first self-tuning prompting framework for news recommendation, leveraging the capabilities of LLMs to perform complex news recommendation tasks. This framework incorporates a news recommender and a prompt optimizer that applies an iterative bootstrapping process to enhance recommendations through automatic prompt engineering. Extensive experimental results with 400 users show that RecPrompt can achieve an improvement of 3.36% in AUC, 10.49% in MRR, 9.64% in nDCG@5, and 6.20% in nDCG@10 compared to deep neural models. Additionally, we introduce TopicScore, a novel metric to assess explainability by evaluating LLM's ability to summarize topics of interest for users. The results show LLM's effectiveness in accurately identifying topics of interest and delivering comprehensive topic-based explanations.

翻译：新闻推荐系统高度依赖自然语言处理（NLP）方法对内容进行分析、理解与分类，从而根据用户兴趣与阅读行为实现个性化推荐。以GPT-4为代表的大语言模型（LLMs）在自然语言理解方面展现出卓越性能，但其在新闻推荐系统中的适用性仍有待验证。本文提出RecPrompt——首个基于大语言模型的自适应提示框架，通过利用LLMs的能力执行复杂新闻推荐任务。该框架包含新闻推荐器与提示优化器，采用迭代式自举过程，通过自动化提示工程持续优化推荐效果。基于400名用户的广泛实验表明，相较于深度神经网络模型，RecPrompt在AUC指标上提升3.36%，MRR提升10.49%，nDCG@5提升9.64%，nDCG@10提升6.20%。此外，本文提出TopicScore这一创新评估指标，通过量化LLMs为用户归纳兴趣主题的能力来衡量推荐结果的可解释性。实验结果表明，LLMs能够有效识别用户兴趣主题，并提供全面的主题驱动型解释。